paragraph40.75ex plus1ex minus.2ex-1emDense self-supervised learning.
Based on the observation that image-level learning does not imply expressive dense representations [1]}, [2]},
dedicated self-supervised dense representation learning has attracted a lot of attention [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}.
DenseCL reformulates the contrastive objective used in MoCo [17]} to work on spatial features by establishing dense correspondences accross views and is currently the state-of-the-art in transfer learning for semantic segmentation on PVOC [13]}.
Other methods resort to defining an unsupervised object prior such as region proposals [19]}, contour detectors [4]}, [6]}, saliency [7]} or object masks [12]}.
For instance, the current state-of-the-art for unsupervised semantic segmentation, MaskContrast [7]}, uses a pretrained saliency estimator to mine positive and negative pixels for contrastive learning.
